In [1]:
library(DESeq2)
library(ggplot2)
library(reshape2)
library(patchwork)
library(ggrepel)
library(cowplot)
library(grid)
library(RColorBrewer)
library(repr)
library(glmpca)
library(pheatmap)
library(PoiClaClu)
library(apeglm)
library(ashr)
library(vsn)
library(dplyr)
library(tidyr)
library(viridis)
library("pheatmap")
library("ReportingTools")
library("BiocParallel")
library(glmpca)
library(emdbook)
library(tidyverse)
register(MulticoreParam(4))
library(sva)
library(RUVSeq)
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In [2]:
# Load count data
count_data <- read.csv("A549_peak_counts.txt",
skip = 1, sep="\t", header=TRUE, stringsAsFactors=FALSE)
head(count_data,2 )
# Remove the ".genomicAllAligned.sorted.bam" suffix from column names
colnames(count_data) <- sub("\\.CLAM.genomicAllAligned\\.sorted\\.bam$", "", colnames(count_data))
head(count_data, 2)
colnames(count_data)
colnames(count_data) <- sub("^([^\\.]+)\\..*", "\\1", colnames(count_data))
head(count_data, 2)
colnames(count_data, 2)
| Geneid | Chr | Start | End | Strand | Length | AC1_IP.AC1_IP.CLAM.genomicAllAligned.sorted.bam | AC1_NP.AC1_NP.CLAM.genomicAllAligned.sorted.bam | AC2_IP.AC2_IP.CLAM.genomicAllAligned.sorted.bam | AC2_NP.AC2_NP.CLAM.genomicAllAligned.sorted.bam | AP1_IP.AP1_IP.CLAM.genomicAllAligned.sorted.bam | AP1_NP.AP1_NP.CLAM.genomicAllAligned.sorted.bam | AP2_IP.AP2_IP.CLAM.genomicAllAligned.sorted.bam | AP2_NP.AP2_NP.CLAM.genomicAllAligned.sorted.bam | AV1_IP.AV1_IP.CLAM.genomicAllAligned.sorted.bam | AV1_NP.AV1_NP.CLAM.genomicAllAligned.sorted.bam | AV2_IP.AV2_IP.CLAM.genomicAllAligned.sorted.bam | AV2_NP.AV2_NP.CLAM.genomicAllAligned.sorted.bam | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | chr1;chr1;chr1;chr1 | 14621;14721;19121;19221 | 14721;14821;19221;19321 | +;+;+;+ | 402 | 3 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| 2 | ENSG00000310526 | chr1;chr1;chr1;chr1 | 14656;14756;19156;19256 | 14756;14856;19256;19356 | -;-;-;- | 402 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Geneid | Chr | Start | End | Strand | Length | AC1_IP.AC1_IP | AC1_NP.AC1_NP | AC2_IP.AC2_IP | AC2_NP.AC2_NP | AP1_IP.AP1_IP | AP1_NP.AP1_NP | AP2_IP.AP2_IP | AP2_NP.AP2_NP | AV1_IP.AV1_IP | AV1_NP.AV1_NP | AV2_IP.AV2_IP | AV2_NP.AV2_NP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | chr1;chr1;chr1;chr1 | 14621;14721;19121;19221 | 14721;14821;19221;19321 | +;+;+;+ | 402 | 3 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| 2 | ENSG00000310526 | chr1;chr1;chr1;chr1 | 14656;14756;19156;19256 | 14756;14856;19256;19356 | -;-;-;- | 402 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
- 'Geneid'
- 'Chr'
- 'Start'
- 'End'
- 'Strand'
- 'Length'
- 'AC1_IP.AC1_IP'
- 'AC1_NP.AC1_NP'
- 'AC2_IP.AC2_IP'
- 'AC2_NP.AC2_NP'
- 'AP1_IP.AP1_IP'
- 'AP1_NP.AP1_NP'
- 'AP2_IP.AP2_IP'
- 'AP2_NP.AP2_NP'
- 'AV1_IP.AV1_IP'
- 'AV1_NP.AV1_NP'
- 'AV2_IP.AV2_IP'
- 'AV2_NP.AV2_NP'
| Geneid | Chr | Start | End | Strand | Length | AC1_IP | AC1_NP | AC2_IP | AC2_NP | AP1_IP | AP1_NP | AP2_IP | AP2_NP | AV1_IP | AV1_NP | AV2_IP | AV2_NP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <chr> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | chr1;chr1;chr1;chr1 | 14621;14721;19121;19221 | 14721;14821;19221;19321 | +;+;+;+ | 402 | 3 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| 2 | ENSG00000310526 | chr1;chr1;chr1;chr1 | 14656;14756;19156;19256 | 14756;14856;19256;19356 | -;-;-;- | 402 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
- 'Geneid'
- 'Chr'
- 'Start'
- 'End'
- 'Strand'
- 'Length'
- 'AC1_IP'
- 'AC1_NP'
- 'AC2_IP'
- 'AC2_NP'
- 'AP1_IP'
- 'AP1_NP'
- 'AP2_IP'
- 'AP2_NP'
- 'AV1_IP'
- 'AV1_NP'
- 'AV2_IP'
- 'AV2_NP'
In [3]:
count_data2 <- count_data %>%
mutate(
Chr = sapply(strsplit(Chr, ";"), `[`, 1),
Start = sapply(strsplit(Start, ";"), function(x) min(as.integer(x))),
End = sapply(strsplit(End, ";"), function(x) max(as.integer(x)))
)
colnames(count_data2)
head(count_data2, 2)
- 'Geneid'
- 'Chr'
- 'Start'
- 'End'
- 'Strand'
- 'Length'
- 'AC1_IP'
- 'AC1_NP'
- 'AC2_IP'
- 'AC2_NP'
- 'AP1_IP'
- 'AP1_NP'
- 'AP2_IP'
- 'AP2_NP'
- 'AV1_IP'
- 'AV1_NP'
- 'AV2_IP'
- 'AV2_NP'
| Geneid | Chr | Start | End | Strand | Length | AC1_IP | AC1_NP | AC2_IP | AC2_NP | AP1_IP | AP1_NP | AP2_IP | AP2_NP | AV1_IP | AV1_NP | AV2_IP | AV2_NP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <int> | <int> | <chr> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | chr1 | 14621 | 19321 | +;+;+;+ | 402 | 3 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| 2 | ENSG00000310526 | chr1 | 14656 | 19356 | -;-;-;- | 402 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
In [ ]:
In [4]:
# Select _IP columns + Geneid
ip <- count_data2 %>% select(Geneid, ends_with("_IP"))
head(ip, 2)
# Select _NP columns + Geneid
np <- count_data2 %>% select(Geneid, ends_with("_NP"))
head(np, 2)
| Geneid | AC1_IP | AC2_IP | AP1_IP | AP2_IP | AV1_IP | AV2_IP | |
|---|---|---|---|---|---|---|---|
| <chr> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | 3 | 0 | 0 | 0 | 2 | 0 |
| 2 | ENSG00000310526 | 0 | 0 | 3 | 0 | 0 | 0 |
| Geneid | AC1_NP | AC2_NP | AP1_NP | AP2_NP | AV1_NP | AV2_NP | |
|---|---|---|---|---|---|---|---|
| <chr> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 1 | ENSG00000290825 | 1 | 2 | 0 | 0 | 1 | 0 |
| 2 | ENSG00000310526 | 0 | 2 | 0 | 0 | 1 | 0 |
In [ ]:
In [5]:
print("!!!")
[1] "!!!"
In [6]:
print("In this analysis, we focus on RNA-seq data that overlaps with peak regions. For simplicity, we assign the variable ip to nip.")
[1] "In this analysis, we focus on RNA-seq data that overlaps with peak regions. For simplicity, we assign the variable ip to nip."
In [7]:
print("!!!")
[1] "!!!"
In [ ]:
In [8]:
print("DESeq2 to measure the differential binding across non-IP samples (RNA-seq) :")
[1] "DESeq2 to measure the differential binding across non-IP samples (RNA-seq) :"
In [9]:
# Step 1: Select only columns ending with "_NP" and include Geneid for rownames
print("!!! _NP")
ip <- count_data2 %>%
select(Geneid, ends_with("_NP"))
# Step 2: Set Geneid as rownames and remove Geneid column
rownames(ip) <- ip$Geneid
ip <- ip[, -1]
# Step 3: Verify column names
sample_names <- colnames(ip)
print(sample_names)
# Step 4: Assign condition groups (AC, AP, AV)
conditions <- ifelse(grepl("^AC", sample_names), "AC",
ifelse(grepl("^AP", sample_names), "AP",
ifelse(grepl("^AV", sample_names), "AV", NA)))
print(conditions)
# Step 5: Error handling if any unrecognized sample names
if (any(is.na(conditions))) {
stop("Some sample names do not match expected patterns (AC, AP, AV). Check column names!")
}
# Step 6: Create colData
col_data <- data.frame(
row.names = sample_names,
condition = factor(conditions, levels = c("AC", "AP", "AV"))
)
print("col data:")
print(col_data)
# Step 7: Remove rows with NA values
dim(ip)
ip <- ip[complete.cases(ip), ]
dim(ip)
# Step 8: Compute summary statistics directly on ip (only numeric columns)
summary_stats <- data.frame(
Median = apply(ip, 2, median, na.rm = TRUE),
Min = apply(ip, 2, min, na.rm = TRUE),
Max = apply(ip, 2, max, na.rm = TRUE)
)
# Step 9: Print the result
print(summary_stats)
[1] "!!! _NP"
[1] "AC1_NP" "AC2_NP" "AP1_NP" "AP2_NP" "AV1_NP" "AV2_NP"
[1] "AC" "AC" "AP" "AP" "AV" "AV"
[1] "col data:"
condition
AC1_NP AC
AC2_NP AC
AP1_NP AP
AP2_NP AP
AV1_NP AV
AV2_NP AV
- 11828
- 6
- 11828
- 6
Median Min Max AC1_NP 99.0 0 180293 AC2_NP 111.0 0 128386 AP1_NP 51.5 0 87342 AP2_NP 39.0 0 56964 AV1_NP 170.0 0 504805 AV2_NP 124.0 0 411936
In [10]:
# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(countData = ip, colData = col_data, design = ~condition)
# Set reference level for condition (AV will be the baseline)
dds$condition <- relevel(dds$condition, ref = "AV")
# Print number of genes before filtering
cat("Number of genes before filtering:", nrow(dds), "\n")
# Estimate size factors (required for normalization)
dds <- estimateSizeFactors(dds)
# Filter: keep genes with normalized count >= 4 in at least 4 samples
keep <- rowSums(counts(dds, normalized = TRUE) >= 4) >= 4
dds <- dds[keep, ]
# Print number of genes after filtering
cat("Number of genes after filtering:", nrow(dds), "\n")
# Run DESeq2 differential expression analysis
dds <- DESeq(dds)
# Extract results table
# res <- results(dds)
# it will produce : Wald test p-value: condition AC vs AV
# View summary of results
# cat("First row of DE results:\n")
# print(head(res, 1))
# cat("Last row of DE results:\n")
# print(tail(res, 1))
# cat("Summary of DESeq2 results:\n")
# print(summary(res))
# Show contrast names
cat("Available result contrasts:\n")
print(resultsNames(dds))
# Size factors (re-estimation here is harmless but redundant)
cat("The size factors are:\n")
print(sizeFactors(dds))
# Extract normalized counts
norm_counts <- counts(dds, normalized = TRUE)
# Preview normalized counts
cat("Preview of normalized counts:\n")
print(head(norm_counts, 2))
# Save normalized counts to CSV
write.csv(norm_counts, "A549.peaks.nonIP.samples.normalized.counts.csv", row.names = TRUE)
# Compute summary statistics for each sample (column)
summary_stats2 <- data.frame(
Median = apply(norm_counts, 2, median, na.rm = TRUE),
Min = apply(norm_counts, 2, min, na.rm = TRUE),
Max = apply(norm_counts, 2, max, na.rm = TRUE)
)
# Print summary statistics
cat("Summary of the normalized counts:\n")
print(round(summary_stats2, 2))
# Show available assays in the DESeqDataSet
cat("Available assays in dds:\n")
print(names(assays(dds)))
# View fitted means (mu) and Cook's distances
cat("DESeq2 fitted means (mu):\n")
print(head(assay(dds, "mu"), 2))
cat("DESeq2 Cook's distances (cooks):\n")
print(head(assay(dds, "cooks"), 2))
Number of genes before filtering: 11828 Number of genes after filtering: 9926
using pre-existing size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing
Available result contrasts:
[1] "Intercept" "condition_AC_vs_AV" "condition_AP_vs_AV"
The size factors are:
AC1_NP AC2_NP AP1_NP AP2_NP AV1_NP AV2_NP
1.1702662 1.2646608 0.6044499 0.4609352 1.7948441 1.4007098
Preview of normalized counts:
AC1_NP AC2_NP AP1_NP AP2_NP AV1_NP AV2_NP
ENSG00000310528 9.399571 16.60524 8.271985 15.18652 9.471575 10.70886
ENSG00000310527 32.471245 36.37339 26.470351 28.20353 30.643331 27.12910
Summary of the normalized counts:
Median Min Max
AC1_NP 124.76 0 154061.5
AC2_NP 129.68 0 101518.1
AP1_NP 127.39 0 144498.3
AP2_NP 128.00 0 123583.5
AV1_NP 138.73 0 281252.8
AV2_NP 134.22 0 294090.9
Available assays in dds:
[1] "counts" "mu" "H" "cooks"
DESeq2 fitted means (mu):
AC1_NP AC2_NP AP1_NP AP2_NP AV1_NP AV2_NP
ENSG00000310528 15.24734 16.47720 6.982534 5.324669 18.08434 14.11316
ENSG00000310527 40.29876 43.54929 16.496758 12.579929 51.90972 40.51073
DESeq2 Cook's distances (cooks):
AC1_NP AC2_NP AP1_NP AP2_NP AV1_NP
ENSG00000310528 0.47925143 0.51001246 0.342638480 0.248675262 0.02698987
ENSG00000310527 0.03279535 0.03422119 0.007025132 0.005334472 0.04235784
AV2_NP
ENSG00000310528 0.02214060
ENSG00000310527 0.03727555
In [11]:
# Extract raw (unnormalized) counts
print("raw counts")
raw_counts <- counts(dds, normalized = FALSE)
head(raw_counts, 2)
# Extract normalized counts
print("norm counts")
norm_counts <- counts(dds, normalized = TRUE)
head(norm_counts, 2)
[1] "raw counts"
| AC1_NP | AC2_NP | AP1_NP | AP2_NP | AV1_NP | AV2_NP | |
|---|---|---|---|---|---|---|
| ENSG00000310528 | 11 | 21 | 5 | 7 | 17 | 15 |
| ENSG00000310527 | 38 | 46 | 16 | 13 | 55 | 38 |
[1] "norm counts"
| AC1_NP | AC2_NP | AP1_NP | AP2_NP | AV1_NP | AV2_NP | |
|---|---|---|---|---|---|---|
| ENSG00000310528 | 9.399571 | 16.60524 | 8.271985 | 15.18652 | 9.471575 | 10.70886 |
| ENSG00000310527 | 32.471245 | 36.37339 | 26.470351 | 28.20353 | 30.643331 | 27.12910 |
In [12]:
print("Boxplot of Raw vs log2 Normalized Counts")
# Prepare raw counts
raw_counts <- as.data.frame(counts(dds, normalized = FALSE))
raw_counts$Gene <- rownames(raw_counts)
raw_long <- pivot_longer(raw_counts, -Gene, names_to = "Sample", values_to = "Count")
raw_long$log2_count <- log2(raw_long$Count + 1)
# Prepare normalized counts
norm_counts <- as.data.frame(counts(dds, normalized = TRUE))
norm_counts$Gene <- rownames(norm_counts)
norm_long <- pivot_longer(norm_counts, -Gene, names_to = "Sample", values_to = "Count")
norm_long$log2_count <- log2(norm_long$Count + 1)
# Color palette
sample_list <- unique(c(raw_long$Sample, norm_long$Sample))
sample_colors <- setNames(viridis::viridis(length(sample_list), option = "D"), sample_list)
# Plot p1: Raw counts
p1 <- ggplot(raw_long, aes(x = Sample, y = log2_count, fill = Sample)) +
geom_boxplot(outlier.size = 0.5, width = 0.7) +
scale_fill_manual(values = sample_colors, name = "Sample") +
theme_minimal(base_size = 12) +
labs(title = "Raw Counts (log2 scale)",
y = "log2(Counts + 1)", x = "Sample") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot p2: Normalized counts
p2 <- ggplot(norm_long, aes(x = Sample, y = log2_count, fill = Sample)) +
geom_boxplot(outlier.size = 0.5, width = 0.7) +
scale_fill_manual(values = sample_colors, name = "Sample") +
theme_minimal(base_size = 12) +
labs(title = "Normalized Counts (log2 scale)",
y = "log2(Counts + 1)", x = "Sample") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)
# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')
[1] "Boxplot of Raw vs log2 Normalized Counts"
In [13]:
# Extract raw counts and log-transform
raw_counts <- counts(dds, normalized = FALSE)
raw_log_counts <- log10(raw_counts + 1)
# Convert to long format for ggplot2
log1_df <- as.data.frame(raw_log_counts)
log1_df$Gene <- rownames(log1_df)
log1_long <- pivot_longer(log1_df, -Gene, names_to = "Sample", values_to = "log10_count")
# Extract normalized counts and log-transform
norm_counts <- counts(dds, normalized = TRUE)
norm_log_counts <- log10(norm_counts + 1)
# Convert to long format for ggplot2
log2_df <- as.data.frame(norm_log_counts)
log2_df$Gene <- rownames(log2_df)
log2_long <- pivot_longer(log2_df, -Gene, names_to = "Sample", values_to = "log10_count")
# Plot with ggplot2
p1 = ggplot(log1_long, aes(x = log10_count, color = Sample)) +
geom_density(size = 1.2, alpha = 0.8) +
theme_minimal(base_size = 14) +
labs(
title = "Density Plot of log10 Raw Counts (Before Normalization)",
x = "log10(Counts + 1)",
y = "Density"
) +
theme(
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "right"
)
# Plot with ggplot2
p2 = ggplot(log2_long, aes(x = log10_count, color = Sample)) +
geom_density(size = 1.2, alpha = 0.8) +
theme_minimal(base_size = 14) +
labs(
title = "Density Plot of log10 Normalized Counts",
x = "log10(Normalized Counts + 1)",
y = "Density"
) +
theme(
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "right"
)
# Combine side-by-side
options(repr.plot.width = 14, repr.plot.height = 6)
# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')
Warning message:
"Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead."
In [14]:
print("printing the results")
resultsNames(dds)
as.data.frame(colData(dds))
head(rowData(dds), 3)
# or
# head(mcols(dds), 3)
[1] "printing the results"
- 'Intercept'
- 'condition_AC_vs_AV'
- 'condition_AP_vs_AV'
| condition | sizeFactor | |
|---|---|---|
| <fct> | <dbl> | |
| AC1_NP | AC | 1.1702662 |
| AC2_NP | AC | 1.2646608 |
| AP1_NP | AP | 0.6044499 |
| AP2_NP | AP | 0.4609352 |
| AV1_NP | AV | 1.7948441 |
| AV2_NP | AV | 1.4007098 |
DataFrame with 3 rows and 26 columns
baseMean baseVar allZero dispGeneEst dispGeneIter
<numeric> <numeric> <logical> <numeric> <numeric>
ENSG00000310528 11.6073 11.8314 FALSE 0.00000001 7
ENSG00000310527 30.2152 14.1582 FALSE 0.00000001 10
ENSG00000225880 415.7271 80149.0210 FALSE 0.08126658 7
dispFit dispersion dispIter dispOutlier dispMAP Intercept
<numeric> <numeric> <integer> <logical> <numeric> <numeric>
ENSG00000310528 0.3362446 0.2690714 9 FALSE 0.2690714 3.33281
ENSG00000310527 0.1500427 0.1126660 7 FALSE 0.1126660 4.85407
ENSG00000225880 0.0423348 0.0519857 10 FALSE 0.0519857 9.53041
condition_AC_vs_AV condition_AP_vs_AV SE_Intercept
<numeric> <numeric> <numeric>
ENSG00000310528 0.370838 0.1972451 0.587767
ENSG00000310527 0.251753 -0.0836581 0.374248
ENSG00000225880 -1.620226 -1.4718819 0.234510
SE_condition_AC_vs_AV SE_condition_AP_vs_AV
<numeric> <numeric>
ENSG00000310528 0.831370 0.892491
ENSG00000310527 0.531192 0.574203
ENSG00000225880 0.335640 0.341383
WaldStatistic_Intercept WaldStatistic_condition_AC_vs_AV
<numeric> <numeric>
ENSG00000310528 5.67029 0.446057
ENSG00000310527 12.97021 0.473939
ENSG00000225880 40.63971 -4.827277
WaldStatistic_condition_AP_vs_AV WaldPvalue_Intercept
<numeric> <numeric>
ENSG00000310528 0.221005 1.42557e-08
ENSG00000310527 -0.145694 1.80527e-38
ENSG00000225880 -4.311523 0.00000e+00
WaldPvalue_condition_AC_vs_AV WaldPvalue_condition_AP_vs_AV
<numeric> <numeric>
ENSG00000310528 6.55556e-01 8.25088e-01
ENSG00000310527 6.35544e-01 8.84163e-01
ENSG00000225880 1.38413e-06 1.62134e-05
betaConv betaIter deviance maxCooks
<logical> <numeric> <numeric> <logical>
ENSG00000310528 TRUE 3 35.3016 NA
ENSG00000310527 TRUE 2 40.7696 NA
ENSG00000225880 TRUE 3 68.9386 NA
In [15]:
message("What is Cook’s distance?\n\n",
"Cook's distance comes from regression analysis. It tells you how much a single data point (sample) influences the estimated coefficients (i.e., the log2 fold changes) for a gene.\n\n",
"In the context of DESeq2, Cook's distance is computed per gene per sample.\n",
"It quantifies how much the removal of that sample would change the fitted model for the gene.\n")
What is Cook’s distance? Cook's distance comes from regression analysis. It tells you how much a single data point (sample) influences the estimated coefficients (i.e., the log2 fold changes) for a gene. In the context of DESeq2, Cook's distance is computed per gene per sample. It quantifies how much the removal of that sample would change the fitted model for the gene.
In [16]:
print("No shrinkage")
# Get results for different comparisons
res_AP_vs_AC <- results(dds, contrast = c("condition", "AP", "AC"))
res_AP_vs_AV <- results(dds, contrast = c("condition", "AP", "AV"))
res_AC_vs_AV <- results(dds, contrast = c("condition", "AC", "AV"))
# summary(res_AP_vs_AC)
# summary(res_AC_vs_AV)
# summary(res_AP_vs_AV)
# Save results
write.csv(as.data.frame(res_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.csv")
write.csv(as.data.frame(res_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.csv")
write.csv(as.data.frame(res_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.csv")
###########################################################
###########################################################
print("number of differentially bound transcripts : AP vs AC : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(res_AP_vs_AC, padj < 0.1))[1]
print("number of differentially bound transcripts : AP vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(res_AP_vs_AV, padj < 0.1))[1]
print("number of differentially bound transcripts : AC vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(res_AC_vs_AV, padj < 0.1))[1]
###########################################################
###########################################################
[1] "No shrinkage" [1] "number of differentially bound transcripts : AP vs AC : pvalue < 0.05 and padj < 0.1"
281
11
[1] "number of differentially bound transcripts : AP vs AV : pvalue < 0.05 and padj < 0.1"
4700
4649
[1] "number of differentially bound transcripts : AC vs AV : pvalue < 0.05 and padj < 0.1"
4786
4751
In [17]:
# type = c("apeglm", "ashr", "normal")
In [18]:
print("Data shrinkage : normal lfcShrink")
[1] "Data shrinkage : normal lfcShrink"
In [19]:
# Get results for different comparisons
resLFCnormal_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="normal")
resLFCnormal_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="normal")
resLFCnormal_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="normal")
# summary(resLFCnormal_AP_vs_AC)
# summary(resLFCnormal_AC_vs_AV)
# summary(resLFCnormal_AP_vs_AV)
# Save results
write.csv(as.data.frame(resLFCnormal_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.resLFCnormal.csv")
###########################################################
###########################################################
print("number of differentially bound and expressed transcripts : resLFCnormal: AP vs AC : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AP_vs_AC, pvalue < 0.05))
dim(subset(resLFCnormal_AP_vs_AC, padj < 0.1))
print("number of differentially bound and expressed transcripts : resLFCnormal : AP vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AP_vs_AV, pvalue < 0.05))
dim(subset(resLFCnormal_AP_vs_AV, padj < 0.1))
print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AC_vs_AV, pvalue < 0.05))
dim(subset(resLFCnormal_AC_vs_AV, padj < 0.1))
###########################################################
###########################################################
using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014). Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'. See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette. Reference: https://doi.org/10.1093/bioinformatics/bty895 using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014). Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'. See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette. Reference: https://doi.org/10.1093/bioinformatics/bty895 using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014). Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'. See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette. Reference: https://doi.org/10.1093/bioinformatics/bty895
[1] "number of differentially bound and expressed transcripts : resLFCnormal: AP vs AC : pvalue < 0.05 and padj < 0.1"
- 281
- 6
- 11
- 6
[1] "number of differentially bound and expressed transcripts : resLFCnormal : AP vs AV : pvalue < 0.05 and padj < 0.1"
- 4700
- 6
- 4649
- 6
[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 0.1"
- 4786
- 6
- 4751
- 6
In [20]:
print("Data shrinkage : ashr lfcShrink")
[1] "Data shrinkage : ashr lfcShrink"
In [21]:
# If you must use contrast, you should use type="normal" or type="ashr" instead of apeglm,
# because apeglm only works with coef.
# Apeglm is the recommended method for log-fold change shrinkage.
# Get results for different comparisons
# resLFCapeglm_AP_vs_AV <- lfcShrink(dds, coef = "condition_AP_vs_AV", type="apeglm")
# resLFCapeglm_AC_vs_AV <- lfcShrink(dds, coef = "condition_AC_vs_AV", type="apeglm")
resLFCashr_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="ashr")
resLFCashr_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="ashr")
resLFCashr_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="ashr")
# summary(resLFCashr_AP_vs_AC)
# summary(resLFCashr_AC_vs_AV)
# summary(resLFCashr_AP_vs_AV)
# Save results
write.csv(as.data.frame(resLFCashr_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.resLFCashr.csv")
###########################################################
###########################################################
print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AC, padj < 0.1))[1]
print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 ")
dim(subset(resLFCashr_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AV, padj < 0.1))[1]
print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05")
dim(subset(resLFCashr_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AC_vs_AV, padj < 0.1))[1]
###########################################################
###########################################################
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1"
281
11
[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 "
4700
4649
[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05"
4786
4751
In [22]:
# If you must use contrast, you should use type="normal" or type="ashr" instead of apeglm,
# because apeglm only works with coef.
# Apeglm is the recommended method for log-fold change shrinkage.
# Get results for different comparisons
# resLFCapeglm_AP_vs_AV <- lfcShrink(dds, coef = "condition_AP_vs_AV", type="apeglm")
# resLFCapeglm_AC_vs_AV <- lfcShrink(dds, coef = "condition_AC_vs_AV", type="apeglm")
resLFCashr_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="ashr")
resLFCashr_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="ashr")
resLFCashr_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="ashr")
# summary(resLFCashr_AP_vs_AC)
# summary(resLFCashr_AC_vs_AV)
# summary(resLFCashr_AP_vs_AV)
# Save results
write.csv(as.data.frame(resLFCashr_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.resLFCashr.csv")
###########################################################
###########################################################
print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AC, padj < 0.1))[1]
print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AV, padj < 0.1))[1]
print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AC_vs_AV, padj < 0.1))[1]
###########################################################
###########################################################
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041
[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1"
281
11
[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 and padj < 1"
4700
4649
[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 1"
4786
4751
In [ ]:
In [23]:
print("Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05")
# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
# The information about DE peaks was stored in :
# res_AP_vs_AC
# res_AP_vs_AV
# res_AC_vs_AV
# resLFCnormal_AP_vs_AV
# resLFCnormal_AC_vs_AV
# resLFCnormal_AP_vs_AC
# resLFCashr_AP_vs_AV
# resLFCashr_AC_vs_AV
# resLFCashr_AP_vs_AC
# Count DEGs
n_DE_unshrunken <- sum(res_AP_vs_AC$pvalue < 0.05, na.rm = TRUE)
n_DE_shrink_normal <- sum(resLFCnormal_AP_vs_AC$pvalue < 0.05, na.rm = TRUE) # Same p-values as unshrunken
n_DE_ashr <- sum( resLFCashr_AP_vs_AC$pvalue < 0.05, na.rm = TRUE) # Same p-values as unshrunken
# Build a data frame
compare_df1 <- data.frame(
Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
DE_Genes = c(n_DE_unshrunken, n_DE_shrink_normal, n_DE_ashr)
)
# Plot it
p1 = ggplot(compare_df1, aes(x = Method, y = DE_Genes, fill = Method)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(
title = "Comparison of DE Gene Counts (AC vs AP)",
y = "Number of DE Genes (p < 0.05 or s < 0.05)",
x = "Shrinkage Method"
) +
theme(legend.position = "none") +
geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4)
print("Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05")
# Shrinkage Methods: The lfcShrink() function in DESeq2 is used to obtain more accurate estimates of log2 fold changes,
# especially for genes with low counts or high variability.
# Threshold Selection: The choice of a log2FC threshold (e.g., 0.3) is somewhat arbitrary and should be based on the biological context
# and the desired stringency of the analysis.
# Interpretation: Comparing the number of DE genes across different shrinkage methods can provide insights into the robustness of your findings.
# It's common to observe variations in the number of DE genes identified, depending on the method used.
# Raw (non-shrunk)
n_raw <- sum(res_AP_vs_AC$pvalue < pval_cutoff & abs(res_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Normal shrink
n_normal <- sum(res_AP_vs_AC$pvalue < pval_cutoff & abs(resLFCnormal_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Ashr shrink (using s-value instead of p-value)
n_ashr <- sum(resLFCashr_AP_vs_AC$pvalue < pval_cutoff & abs(resLFCashr_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Combine into a data frame
compare_df2 <- data.frame(
Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
DE_Genes = c(n_raw, n_normal, n_ashr)
)
p2 = ggplot(compare_df2, aes(x = Method, y = DE_Genes, fill = Method)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(
title = "DE Gene Counts (AP vs AC)",
subtitle = "p < 0.05 and |log2FC| > 0.5",
y = "Number of DE Genes",
x = "Method"
) +
geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4) +
theme(legend.position = "none")
# Print the plot in Jupyter
options(repr.plot.width = 8, repr.plot.height = 6)
p1 + p2
[1] "Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05" [1] "Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05"
In [ ]:
In [24]:
print("MA plots:")
# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
[1] "MA plots:"
In [25]:
make_MA_plot <- function(res_df, title = "MA Plot", lfc_cutoff = 0.3, pval_cutoff = 0.1, ylim = c(-2, 2)) {
res_df <- as.data.frame(res_df)
# Replace NA p-values with the threshold so they are not considered significant
res_df$pvalue[is.na(res_df$pvalue)] <- 1
# Label significance based on thresholds
res_df$sig <- ifelse(res_df$pvalue < pval_cutoff & abs(res_df$log2FoldChange) > lfc_cutoff,
"Significant", "Not Significant")
# Generate the MA plot
ggplot(res_df, aes(x = baseMean, y = log2FoldChange, color = sig)) +
geom_point(alpha = 0.6, size = 1) +
scale_x_log10() +
scale_color_manual(values = c("Significant" = "#D7263D", "Not Significant" = "gray70")) +
geom_hline(yintercept = c(-lfc_cutoff, lfc_cutoff), linetype = "dashed", color = "black") +
coord_cartesian(ylim = ylim) +
theme_minimal(base_size = 14) +
labs(
title = title,
x = "Mean Expression (log10 scale)",
y = "log2 Fold Change",
color = "Significance"
) +
theme(
legend.position = "right",
panel.grid.minor = element_blank()
)
}
In [26]:
print("MA plots:")
print("AP vs AC")
# Create the plots
p1 <- make_MA_plot(res_AP_vs_AC, title = "AP vs AC (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AP_vs_AC, title = "AP vs AC (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AP_vs_AC, title = "AP vs AC (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 6)
# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')
[1] "MA plots:" [1] "AP vs AC"
In [27]:
print("MA plots:")
print("AC vs AV")
# Create the plots
p1 <- make_MA_plot(res_AC_vs_AV, title = "AC vs AV (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AC_vs_AV, title = "AC vs AV (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AC_vs_AV, title = "AC vs AV (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)
# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')
[1] "MA plots:" [1] "AC vs AV"
In [28]:
print("MA plots:")
print("AP vs AV")
# Create the plots
p1 <- make_MA_plot(res_AP_vs_AV, title = "AP vs AV (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AP_vs_AV, title = "AP vs AV (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AP_vs_AV, title = "AP vs AV (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)
# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')
[1] "MA plots:" [1] "AP vs AV"
In [ ]:
In [29]:
print("plotting dispersion")
# Combine side-by-side
options(repr.plot.width = 6, repr.plot.height = 6)
plotDispEsts(dds)
dispersionFunction(dds)
[1] "plotting dispersion"
structure(function (q)
coefs[1] + coefs[2]/q, coefficients = c(asymptDisp = 0.0338929572374192,
extraPois = 3.50948342483534), fitType = "parametric", varLogDispEsts = 0.749835307458086, dispPriorVar = 0.25)
In [ ]:
In [30]:
print("RLD and VST transformations")
# Effects of transformations on the variance
rld <- rlog(dds, blind = FALSE)
vsd <- vst(dds, blind = FALSE)
ntd <- normTransform(dds)
# meanSdPlot(assay(ntd))
# meanSdPlot(assay(rld))
# meanSdPlot(assay(vsd))
[1] "RLD and VST transformations"
In [31]:
library("pheatmap")
# Select the top 20 differentially expressed genes based on adjusted p-value
top_genes <- rownames(res_AP_vs_AC)[order(res_AP_vs_AC$padj, na.last=NA)][1:20] #
# Extract normalized transformed counts for the top genes
top_counts <- assay(vsd)[top_genes, ]
# Create annotation dataframe
df <- as.data.frame(colData(dds)["condition"]) # Ensure it is a proper dataframe
colnames(df) <- "Condition" # Rename column for clarity
# Generate heatmap
options(repr.plot.width = 6, repr.plot.height = 6)
pheatmap(top_counts,
cluster_rows=TRUE, # Cluster rows to group similar genes
show_rownames=TRUE, # Show gene names
cluster_cols=TRUE, # Cluster samples
annotation_col=df, # Add sample condition annotations
scale="row", # Normalize each gene's expression across samples
fontsize_row=8) # Adjust row text size for readability
In [32]:
print("PCA and MDS plots of rlog- and vst-transformed counts")
[1] "PCA and MDS plots of rlog- and vst-transformed counts"
In [33]:
# 1. rlog transformation and PCA
rld <- rlog(dds, blind = FALSE)
pca_rld <- plotPCA(rld, intgroup = "condition", returnData = TRUE)
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))
pca_rld_plot <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
geom_point(size = 4) +
scale_color_brewer(palette = "Dark2") +
xlab(paste0("PC1: ", percentVar_rld[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_rld[2], "% variance")) +
ggtitle("PCA (rlog)") +
theme_minimal()
# 2. rlog MDS
dists_rld <- dist(t(assay(rld)))
mds_rld <- cmdscale(as.matrix(dists_rld))
mds_rld_df <- data.frame(MDS1 = mds_rld[,1], MDS2 = mds_rld[,2], condition = col_data$condition)
mds_rld_plot <- ggplot(mds_rld_df, aes(MDS1, MDS2, color = condition)) +
geom_point(size = 4) +
scale_color_brewer(palette = "Dark2") +
ggtitle("MDS (rlog)") +
theme_minimal()
# 3. vst transformation and PCA
vsd <- vst(dds, blind = FALSE)
pca_vsd <- plotPCA(vsd, intgroup = "condition", returnData = TRUE)
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
pca_vsd_plot <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
geom_point(size = 4) +
scale_color_brewer(palette = "Dark2") +
xlab(paste0("PC1: ", percentVar_vsd[1], "% variance")) +
ylab(paste0("PC2: ", percentVar_vsd[2], "% variance")) +
ggtitle("PCA (vst)") +
theme_minimal()
# 4. vst MDS
dists_vsd <- dist(t(assay(vsd)))
mds_vsd <- cmdscale(as.matrix(dists_vsd))
mds_vsd_df <- data.frame(MDS1 = mds_vsd[,1], MDS2 = mds_vsd[,2], condition = col_data$condition)
mds_vsd_plot <- ggplot(mds_vsd_df, aes(MDS1, MDS2, color = condition)) +
geom_point(size = 4) +
scale_color_brewer(palette = "Dark2") +
ggtitle("MDS (vst)") +
theme_minimal()
# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 8)
# Combine all plots in a 2x2 grid
plot_grid(
pca_rld_plot, mds_rld_plot,
pca_vsd_plot, mds_vsd_plot,
labels = c("A", "B", "C", "D"),
ncol = 2, align = "hv"
)
using ntop=500 top features by variance using ntop=500 top features by variance
In [ ]:
In [34]:
library(pheatmap)
library(RColorBrewer)
library(gridExtra)
library(grid)
# === RLOG Heatmap ===
rlog_matrix <- assay(rld)
sampleDists_rlog <- dist(t(rlog_matrix))
sampleDistMatrix_rlog <- as.matrix(sampleDists_rlog)
colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)
p1 <- pheatmap(sampleDistMatrix_rlog,
clustering_distance_rows = sampleDists_rlog,
clustering_distance_cols = sampleDists_rlog,
col = colors,
fontsize_row = 10,
fontsize_col = 10,
cellwidth = 60,
cellheight = 60,
angle_col = 45,
main = "Sample Distance Heatmap (rlog)",
silent = TRUE)
# === VST Heatmap ===
vsd_matrix <- assay(vsd)
sampleDists_vsd <- dist(t(vsd_matrix))
sampleDistMatrix_vsd <- as.matrix(sampleDists_vsd)
p2 <- pheatmap(sampleDistMatrix_vsd,
clustering_distance_rows = sampleDists_vsd,
clustering_distance_cols = sampleDists_vsd,
col = colors,
fontsize_row = 10,
fontsize_col = 10,
cellwidth = 60,
cellheight = 60,
angle_col = 45,
main = "Sample Distance Heatmap (vst)",
silent = TRUE)
# === Combine with spacing and ensure layout fits ===
grid.newpage() # Ensures fresh drawing surface
# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 10)
# === Convert pheatmap outputs to grobs ===
grob1 <- p1[[4]]
grob2 <- p2[[4]]
# === Combine with cowplot ===
cowplot::plot_grid(grob1, grob2, ncol = 2, rel_widths = c(1, 1))
Attaching package: 'gridExtra'
The following object is masked from 'package:dplyr':
combine
The following object is masked from 'package:Biobase':
combine
The following object is masked from 'package:BiocGenerics':
combine
In [35]:
print("Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances")
# Another option for calculating sample distances is to use the Poisson Distance (Witten 2011), implemented in the PoiClaClu package.
# This measure of dissimilarity between counts also takes the inherent variance structure of counts into consideration when calculating
# the distances between samples. The PoissonDistance function takes the original count matrix (not normalized) with samples as rows
# instead of columns, so we need to transpose the counts in dds.
[1] "Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances"
In [36]:
print("PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale().")
# === GLM-PCA plot ===
gpca <- glmpca(assay(dds), L = 2)
gpca.dat <- gpca$factors
gpca.dat$sample <- colnames(dds)
gpca.dat$condition <- colData(dds)$condition
p_gpca <- ggplot(gpca.dat, aes(x = dim1, y = dim2, color = condition)) +
geom_point(size = 4.5, alpha = 0.85, stroke = 1) +
geom_text_repel(aes(label = sample), size = 4, box.padding = 0.4, max.overlaps = 8) +
coord_fixed() +
theme_minimal(base_size = 16) +
labs(title = "GLM-PCA", x = "GLM-PC1", y = "GLM-PC2") +
scale_color_brewer(palette = "Set2") +
theme(
legend.position = "right",
plot.title = element_text(face = "bold", size = 16)
)
# === Poisson distance heatmap ===
poisd <- PoissonDistance(t(counts(dds)))
samplePoisDistMatrix <- as.matrix(poisd$dd)
sample_names <- colnames(dds)
rownames(samplePoisDistMatrix) <- sample_names
colnames(samplePoisDistMatrix) <- sample_names
colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)
pheat <- pheatmap(samplePoisDistMatrix,
clustering_distance_rows = poisd$dd,
clustering_distance_cols = poisd$dd,
col = colors,
fontsize_row = 10,
fontsize_col = 10,
cellwidth = 50,
cellheight = 50,
angle_col = 45,
main = "Poisson Distance Heatmap",
silent = TRUE)
# Convert to grob for use with cowplot
g_poisson <- ggdraw(grobTree(pheat$gtable)) + theme(plot.margin = margin(5, 5, 5, 5))
# Set display size
options(repr.plot.width = 16, repr.plot.height = 8)
# Combine plots with cleaner spacing
combined_plot <- plot_grid(
g_poisson, p_gpca,
labels = c("A", "B"),
label_size = 16,
nrow = 1,
rel_widths = c(1.2, 1)
)
# Print
print(combined_plot)
[1] "PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale()."
Warning message in glmpca(assay(dds), L = 2): "Reached maximum number of iterations (1000) without numerical convergence. Results may be unreliable."
In [ ]:
In [37]:
print("Performing Surrogate Variable Analysis")
print("SVA analysis")
# SV1, SV2, ... are surrogate variables — latent (hidden) factors estimated from the data that capture unwanted variation
# (like batch effects, technical noise, or hidden biological subtypes).
# You can think of them as "virtual covariates" — constructed purely from the structure of your data —
# that explain sources of variation not included in your model (like treatment or condition).
[1] "Performing Surrogate Variable Analysis" [1] "SVA analysis"
In [38]:
if (FALSE) {
# === Prepare data ===
dat <- counts(dds, normalized = TRUE)
idx <- rowMeans(dat) > 1
dat <- dat[idx, ]
mod <- model.matrix(~ condition, colData(dds))
mod0 <- model.matrix(~ 1, colData(dds))
svseq <- svaseq(dat, mod, mod0, n.sv = 2)
head(svseq$sv, 2)
# Set layout: 1 row, 2 columns
par(
mfrow = c(1, 2),
mar = c(5, 5, 4, 2) + 0.1, # bottom, left, top, right margins
cex.main = 1.4, # title size
cex.axis = 1.1, # axis label size
cex.lab = 1.2, # axis title size
las = 1 # y-axis labels horizontal
)
# Loop through SV1 and SV2
for (i in 1:2) {
stripchart(
svseq$sv[, i] ~ dds$condition,
vertical = TRUE,
method = "jitter",
pch = 21,
bg = "steelblue",
col = "black",
frame.plot = FALSE,
ylim = c(-0.8, 0.8), # fixed y-axis range
main = paste0("Surrogate Variable SV", i),
ylab = "Surrogate Variable Value",
xlab = "Condition",
cex = 1.3
)
abline(h = 0, lty = 2, col = "gray50", lwd = 1.5)
}
}
In [39]:
# Finally, in order to use SVA to remove any effect on the counts from our surrogate variables, we simply add these two surrogate variables
# as columns to the DESeqDataSet and then add them to the design:
if (FALSE) {
ddssva <- dds
ddssva$SV1 <- svseq$sv[,1]
ddssva$SV2 <- svseq$sv[,2]
design(ddssva) <- ~ SV1 + SV2 + condition
ddssva$SV1
ddssva$SV2
# length(ddssva$SV1)
# length(ddssva$SV2)
ddssva <- DESeq(ddssva)
resultsNames(ddssva)
# rowRanges(ddssva)
# colData(ddssva)
# assays(ddssva)
# assay(ddssva)
# length(rowRanges(ddssva))
res_ddssva <- results(ddssva)
resultsNames(res_ddssva)
# Get results for different comparisons
res_ddssva_AP_vs_AC <- results(ddssva, contrast = c("condition", "AP", "AC"))
res_ddssva_AP_vs_AV <- results(ddssva, contrast = c("condition", "AP", "AV"))
res_ddssva_AC_vs_AV <- results(ddssva, contrast = c("condition", "AC", "AV"))
# summary(res_ddssva_AP_vs_AV)
# summary(res_ddssva_AC_vs_AV)
# summary(res_ddssva_AP_vs_AC)
# Save results
write.csv(as.data.frame(res_ddssva_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.sva.csv")
write.csv(as.data.frame(res_ddssva_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.sva.csv")
write.csv(as.data.frame(res_ddssva_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.sva.csv")
###########################################################
###########################################################
print("number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AP_vs_AC, pvalue < 0.05))
dim(subset(res_ddssva_AP_vs_AC, padj < 0.1))
print("number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AP_vs_AV, pvalue < 0.05))
dim(subset(res_ddssva_AP_vs_AV, padj < 0.1))
print("number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AC_vs_AV, pvalue < 0.05))
dim(subset(res_ddssva_AC_vs_AV, padj < 0.1))
}
In [40]:
if (FALSE) {
# Transform count data
vsd2 <- vst(ddssva, blind = TRUE)
rld2 <- rlog(ddssva, blind = TRUE)
# Get PCA data
pca_vsd <- plotPCA(vsd2, intgroup = "condition", returnData = TRUE)
pca_rld <- plotPCA(rld2, intgroup = "condition", returnData = TRUE)
# Variance explained
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))
# PCA plot for VST
p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
geom_point(size = 3, alpha = 0.8) +
labs(
title = "PCA after SVA (VST)",
x = paste0("PC1 (", percentVar_vsd[1], "%)"),
y = paste0("PC2 (", percentVar_vsd[2], "%)")
) +
theme_minimal(base_size = 14) +
scale_color_brewer(palette = "Set2") +
theme(legend.position = "right")
# PCA plot for RLOG
p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
geom_point(size = 3, alpha = 0.8) +
labs(
title = "PCA after SVA (RLOG)",
x = paste0("PC1 (", percentVar_rld[1], "%)"),
y = paste0("PC2 (", percentVar_rld[2], "%)")
) +
theme_minimal(base_size = 14) +
scale_color_brewer(palette = "Set2") +
theme(legend.position = "right")
# Show both plots side by side with legends
options(repr.plot.width = 14, repr.plot.height = 6)
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)
}
In [41]:
print("RUVseq analysis")
[1] "RUVseq analysis"
In [42]:
library(RUVSeq)
library(DESeq2)
# Create SeqExpressionSet from DESeq2 object
set <- newSeqExpressionSet(counts(dds))
# Keep genes with sufficient expression
idx <- rowSums(counts(set) > 5) >= 2
set <- set[idx, ]
# Normalize
set <- betweenLaneNormalization(set, which = "upper")
# Run DESeq2 just to get raw p-values for empirical control genes
dds_temp <- dds[idx, ]
dds_temp <- DESeq(dds_temp)
res_temp <- results(dds_temp)
# Define empirical control genes as those with high p-value (non-DE)
not.sig <- rownames(res_temp)[which(res_temp$pvalue > 0.1)]
empirical <- rownames(set)[rownames(set) %in% not.sig]
# Apply RUVg with k=2 unwanted factors
set <- RUVg(set, empirical, k = 2)
# Add unwanted factors to DESeq2 design
ddsruv <- dds[idx, ] # use filtered genes
ddsruv$W1 <- set$W_1
ddsruv$W2 <- set$W_2
design(ddsruv) <- ~ W1 + W2 + condition
# Run DESeq2 with adjusted design
ddsruv <- DESeq(ddsruv)
# Check model variables
resultsNames(ddsruv)
# Get results for different comparisons
res_ddsruv_AP_vs_AC <- results(ddsruv, contrast = c("condition", "AP", "AC"))
res_ddsruv_AP_vs_AV <- results(ddsruv, contrast = c("condition", "AP", "AV"))
res_ddsruv_AC_vs_AV <- results(ddsruv, contrast = c("condition", "AC", "AV"))
# Save results
write.csv(as.data.frame(res_ddsruv_AP_vs_AC), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AC_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_AP_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AP_vs_AV_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_AC_vs_AV), file = "A549.peaks.nonIP.samples.DESeq2_AC_vs_AV_results.ruv.csv")
# Summary statistics
print("number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AP_vs_AC, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AP_vs_AC, padj < 0.1)))
print("number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AP_vs_AV, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AP_vs_AV, padj < 0.1)))
print("number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AC_vs_AV, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AC_vs_AV, padj < 0.1)))
using pre-existing size factors estimating dispersions found already estimated dispersions, replacing these gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing using pre-existing size factors estimating dispersions found already estimated dispersions, replacing these gene-wise dispersion estimates mean-dispersion relationship final dispersion estimates fitting model and testing
- 'Intercept'
- 'W1'
- 'W2'
- 'condition_AC_vs_AV'
- 'condition_AP_vs_AV'
[1] "number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1" [1] 232 6 [1] 3 6 [1] "number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1" [1] 514 6 [1] 95 6 [1] "number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1" [1] 294 6 [1] 26 6
In [43]:
# Set layout to 1 row, 2 columns (side by side)
par(
mfrow = c(1, 2),
mar = c(4, 4, 3, 1), # margins: bottom, left, top, right
cex.main = 1.2, # title size
cex.axis = 1.0, # axis tick label size
cex.lab = 1.1, # axis title size
las = 1 # horizontal y-axis labels
)
# Loop over W1 and W2
for (i in 1:2) {
stripchart(
pData(set)[, i] ~ dds$condition,
vertical = TRUE,
method = "jitter",
pch = 21,
bg = "steelblue",
col = "black",
frame.plot = FALSE,
main = paste("W", i),
ylab = "Factor Value",
xlab = "Condition",
cex = 1.1
)
abline(h = 0, lty = 2, col = "gray60", lwd = 1)
}
In [44]:
# Transform count data from ddsruv
vsd3 <- vst(ddsruv, blind = TRUE)
rld3 <- rlog(ddsruv, blind = TRUE)
# Get PCA data
pca_vsd <- plotPCA(vsd3, intgroup = "condition", returnData = TRUE)
pca_rld <- plotPCA(rld3, intgroup = "condition", returnData = TRUE)
# Variance explained
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))
# PCA plot for VST
p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
geom_point(size = 3, alpha = 0.8) +
labs(
title = "PCA after RUV (VST)",
x = paste0("PC1 (", percentVar_vsd[1], "%)"),
y = paste0("PC2 (", percentVar_vsd[2], "%)")
) +
theme_minimal(base_size = 14) +
scale_color_brewer(palette = "Set2") +
theme(legend.position = "right")
# PCA plot for RLOG
p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
geom_point(size = 3, alpha = 0.8) +
labs(
title = "PCA after RUV (RLOG)",
x = paste0("PC1 (", percentVar_rld[1], "%)"),
y = paste0("PC2 (", percentVar_rld[2], "%)")
) +
theme_minimal(base_size = 14) +
scale_color_brewer(palette = "Set2") +
theme(legend.position = "right")
# Show both plots side by side with legends
options(repr.plot.width = 14, repr.plot.height = 6)
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)
using ntop=500 top features by variance using ntop=500 top features by variance
In [ ]:
In [45]:
library(EnhancedVolcano)
# Color Label in legend Meaning
# Grey NS Not Significant – the gene did not pass the p-value or log₂FC thresholds
# Green Log₂ FC The gene passed the log₂ fold change cutoff but not the p-value cutoff
# Blue p-value The gene passed the p-value cutoff but not the log₂FC cutoff
# Red p value and log₂ FC The gene passed both p-value and log₂FC thresholds — most interesting hits
# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
# Set up the plotting window size for a more compact layout
options(repr.plot.width = 10, repr.plot.height = 8)
EnhancedVolcano(res_AP_vs_AC,
lab = rownames(res_AP_vs_AC),
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = pval_cutoff,
FCcutoff = lfc_cutoff,
title = 'AP vs AC',
pointSize = 2.0,
labSize = 3.0,
legendPosition = 'right',
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.5,
boxedLabels = FALSE)
Warning message: "ggrepel: 270 unlabeled data points (too many overlaps). Consider increasing max.overlaps"
In [ ]:
In [46]:
library(clusterProfiler)
library(org.Hs.eg.db)
library(GO.db)
library(DO.db)
library(KEGGREST)
library(ReactomePA)
library(enrichplot)
library(dplyr)
library(msigdbr)
library(msigdb)
library(msigdf)
library(msigdbdf)
clusterProfiler v4.14.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
Please cite:
S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang,
W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize
multiomics data. Nature Protocols. 2024, 19(11):3292-3320
Attaching package: 'clusterProfiler'
The following object is masked from 'package:XVector':
slice
The following object is masked from 'package:purrr':
simplify
The following object is masked from 'package:IRanges':
slice
The following object is masked from 'package:S4Vectors':
rename
The following object is masked from 'package:stats':
filter
Loading required package: AnnotationDbi
Attaching package: 'AnnotationDbi'
The following object is masked from 'package:clusterProfiler':
select
The following object is masked from 'package:dplyr':
select
ReactomePA v1.50.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
Please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
reactome pathway analysis and visualization. Molecular BioSystems.
2016, 12(2):477-479
enrichplot v1.26.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
Please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
clusterProfiler: an R package for comparing biological themes among
gene clusters. OMICS: A Journal of Integrative Biology. 2012,
16(5):284-287
In [47]:
# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
fin_name = "A549.peaks.IP.samples."
In [48]:
res <- res_AP_vs_AC
head(res,3)
log2 fold change (MLE): condition AP vs AC
Wald test p-value: condition AP vs AC
DataFrame with 3 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue
<numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000310528 11.6073 -0.173593 0.892624 -0.194475 0.845804
ENSG00000310527 30.2152 -0.335411 0.575978 -0.582332 0.560343
ENSG00000225880 415.7271 0.148345 0.345264 0.429656 0.667446
padj
<numeric>
ENSG00000310528 NA
ENSG00000310527 NA
ENSG00000225880 0.999238
In [49]:
# Filter significant genes
res_sig <- as.data.frame(res) %>%
rownames_to_column("gene") %>%
filter(padj < pval_cutoff & abs(log2FoldChange) > lfc_cutoff)
# Map ENSEMBL IDs to Entrez IDs
gene_ids <- bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
res_merge <- merge(res_sig, gene_ids, by.x = "gene", by.y = "ENSEMBL")
# Prepare named gene list for GSEA
gene_list2 <- setNames(res_merge$log2FoldChange, res_merge$ENTREZID)
gene_list2 <- sort(gene_list2, decreasing = TRUE)
head(gene_list2, 2)
length(gene_list2)
'select()' returned 1:1 mapping between keys and columns Warning message in bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", : "25% of input gene IDs are fail to map..."
- 8334
- 1.77186914106884
- 8970
- 1.66421429873347
6
In [56]:
library(clusterProfiler)
library(org.Hs.eg.db)
library(cowplot)
# Predefine result objects to avoid "not found" error
result <- NULL
result2 <- NULL
# GO Over-Representation Analysis (ORA)
result <- tryCatch({
ego <- enrichGO(gene = gene_ids$ENTREZID,
OrgDb = org.Hs.eg.db,
ont = "BP",
keyType = "ENTREZID",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 1,
readable = TRUE)
# Save results
write.table(ego@result, file = paste0(fin_name, "_GO_OverRepresentation_Results.txt"),
row.names = FALSE, col.names = TRUE, quote = FALSE)
# Save PNG
png(paste0(fin_name, "_GO_OverRepresentation.png"), width = 1000, height = 800)
print(dotplot(ego, showCategory = 20, title = "GO ORA (BP)"))
dev.off()
# Return ggplot object
dotplot(ego, showCategory = 20, title = "GO ORA (BP)")
}, error = function(e) {
cat("Error in GO ORA:", conditionMessage(e), "\n")
NULL
})
# GO Enrichment Analysis (GSEA)
result2 <- tryCatch({
ego2 <- gseGO(gene = gene_list2,
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05)
if (nrow(ego2@result) > 0) {
write.table(ego2@result, file = paste0(fin_name, "_GO_Enrichment_Results.txt"),
row.names = FALSE, col.names = TRUE, quote = FALSE)
png(paste0(fin_name, "_GO_Enrichment_Plot.png"), width = 1000, height = 800)
print(dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)"))
dev.off()
dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)")
} else {
cat("No enriched terms in GSEA under pvalueCutoff.\n")
NULL
}
}, error = function(e) {
cat("Error in GO GSEA:", conditionMessage(e), "\n")
NULL
})
# === Display plots side by side if both exist ===
options(repr.plot.width = 8, repr.plot.height = 24)
if (!is.null(result) && !is.null(result2)) {
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("No enrichment plots to display.\n")
}
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019). preparing geneSet collections... GSEA analysis... no term enriched under specific pvalueCutoff...
No enriched terms in GSEA under pvalueCutoff.
In [ ]:
In [51]:
# === KEGG Over-Representation Analysis (ORA) ===
result <- tryCatch({
kegg_enrich <- enrichKEGG(
gene = gene_ids$ENTREZID,
organism = "hsa",
pAdjustMethod = "BH",
pvalueCutoff = 0.05
)
if (!is.null(kegg_enrich) && nrow(kegg_enrich@result) > 0) {
# Save results
write.table(kegg_enrich@result,
file = paste0(fin_name, "_KEGG_OverRepresentation_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save PNG plot
png(paste0(fin_name, "_KEGG_OverRepresentation_Plot.png"), width = 1000, height = 800)
print(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))
dev.off()
# Return plot object
return(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))
} else {
cat("⚠️ No enriched KEGG terms found in ORA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in KEGG ORA:", conditionMessage(e), "\n")
return(NULL)
})
# === KEGG Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({
kegg_gse <- gseKEGG(
geneList = gene_list2,
organism = "hsa",
minGSSize = 120,
pvalueCutoff = 0.05,
verbose = FALSE
)
if (!is.null(kegg_gse) && nrow(kegg_gse@result) > 0) {
# Save results
write.table(kegg_gse@result,
file = paste0(fin_name, "_KEGG_Enrichment_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save PNG plot
png(paste0(fin_name, "_KEGG_Enrichment_Plot.png"), width = 1000, height = 800)
print(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))
dev.off()
# Return plot object
return(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))
} else {
cat("⚠️ No enriched KEGG terms found in GSEA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in KEGG GSEA:", conditionMessage(e), "\n")
return(NULL)
})
# === Display Plots Nicely ===
# Set default figure size
options(repr.plot.width = 6, repr.plot.height = 6)
if (!is.null(result) && !is.null(result2)) {
# Side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("⚠️ No KEGG enrichment plots to display.\n")
}
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"... Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"... no term enriched under specific pvalueCutoff...
⚠️ No enriched KEGG terms found in GSEA.
NULL
In [52]:
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(pathview) # Optional for WikiPathways
library(dplyr)
library(DOSE)
library(rWikiPathways)
# === WikiPathways Over-Representation Analysis (ORA) ===
result <- tryCatch({
wikipathways_enrich <- enrichWP(
gene = gene_ids$ENTREZID,
organism = "Homo sapiens",
pvalueCutoff = 0.05
)
if (!is.null(wikipathways_enrich) && nrow(wikipathways_enrich@result) > 0) {
# Save results
write.table(wikipathways_enrich@result,
file = paste0(fin_name, "_WikiPathways_ORA_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save plot
png(paste0(fin_name, "_WikiPathways_ORA_Plot.png"), width = 1000, height = 800)
print(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))
dev.off()
# Return plot object
return(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))
} else {
cat("⚠️ No enriched WikiPathways terms found in ORA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in WikiPathways ORA:", conditionMessage(e), "\n")
return(NULL)
})
# === WikiPathways Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({
wikipathways_gse <- gseWP(
gene = gene_list2,
organism = "Homo sapiens",
pvalueCutoff = 0.05
)
if (!is.null(wikipathways_gse) && nrow(wikipathways_gse@result) > 0) {
# Save results
write.table(wikipathways_gse@result,
file = paste0(fin_name, "_WikiPathways_Enrichment_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save plot
png(paste0(fin_name, "_WikiPathways_Enrichment_Plot.png"), width = 1000, height = 800)
print(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))
dev.off()
# Return plot object
return(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))
} else {
cat("⚠️ No enriched WikiPathways terms found in GSEA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in WikiPathways GSEA:", conditionMessage(e), "\n")
return(NULL)
})
# === Display plots ===
options(repr.plot.width = 5, repr.plot.height = 5) # Default size
if (!is.null(result) && !is.null(result2)) {
# Show both plots side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("⚠️ No WikiPathways enrichment plots to display.\n")
}
##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.
The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################
DOSE v4.0.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
Please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an
R/Bioconductor package for Disease Ontology Semantic and Enrichment
analysis. Bioinformatics. 2015, 31(4):608-609
Attaching package: 'rWikiPathways'
The following object is masked from 'package:edgeR':
getCounts
The following object is masked from 'package:GenomeInfoDb':
listOrganisms
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
preparing geneSet collections...
GSEA analysis...
no term enriched under specific pvalueCutoff...
⚠️ No enriched WikiPathways terms found in GSEA.
NULL
In [ ]:
In [53]:
library(ReactomePA)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
# === Reactome Over-Representation Analysis (ORA) ===
result <- tryCatch({
reactome_ora <- enrichPathway(
gene = gene_ids$ENTREZID,
organism = "human",
pAdjustMethod = "BH",
pvalueCutoff = 0.05
)
if (!is.null(reactome_ora) && nrow(reactome_ora@result) > 0) {
# Save results
write.table(reactome_ora@result,
file = paste0(fin_name, "_Reactome_ORA_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save plot
png(paste0(fin_name, "_Reactome_ORA_Plot.png"), width = 1000, height = 800)
print(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))
dev.off()
# Return plot for screen
return(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))
} else {
cat("⚠️ No enriched Reactome terms found in ORA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in Reactome ORA:", conditionMessage(e), "\n")
return(NULL)
})
# === Reactome GSEA Analysis ===
result2 <- tryCatch({
reactome_gsea <- gsePathway(
gene = gene_list2,
organism = "human",
pAdjustMethod = "BH",
pvalueCutoff = 0.05
)
if (!is.null(reactome_gsea) && nrow(reactome_gsea@result) > 0) {
# Save results
write.table(reactome_gsea@result,
file = paste0(fin_name, "_Reactome_Enrichment_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
# Save plot
png(paste0(fin_name, "_Reactome_Enrichment_Plot.png"), width = 1000, height = 800)
print(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))
dev.off()
# Return plot for screen
return(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))
} else {
cat("⚠️ No enriched Reactome terms found in GSEA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in Reactome GSEA:", conditionMessage(e), "\n")
return(NULL)
})
# === Display plots ===
options(repr.plot.width = 8, repr.plot.height = 6)
if (!is.null(result) && !is.null(result2)) {
# Show both side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("⚠️ No Reactome enrichment plots to display.\n")
}
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019). preparing geneSet collections... GSEA analysis... no term enriched under specific pvalueCutoff...
⚠️ No enriched Reactome terms found in GSEA.
NULL
In [54]:
library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)
# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C2")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)
# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID # for ORA
# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({
msig_enrich <- enricher(
gene = gene_list,
TERM2GENE = C2_t2g
)
if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {
write.table(msig_enrich@result,
file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
dev.off()
return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
} else {
cat("⚠️ No significant MSigDB pathways found in ORA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
return(NULL)
})
# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({
msig_gsea <- GSEA(
geneList = gene_list2,
TERM2GENE = C2_t2g,
pvalueCutoff = 0.05
)
if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {
write.table(msig_gsea@result,
file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
dev.off()
return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
} else {
cat("⚠️ No significant MSigDB GSEA pathways found.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
return(NULL)
})
# === Display plots ===
options(repr.plot.width = 20, repr.plot.height = 24)
if (!is.null(result) && !is.null(result2)) {
options(repr.plot.width = 12, repr.plot.height = 6)
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("⚠️ No MSigDB enrichment plots to display.\n")
}
Warning message:
"The `category` argument of `msigdbr()` is deprecated as of msigdbr 9.0.0.
ℹ Please use the `collection` argument instead."
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
preparing geneSet collections...
GSEA analysis...
no term enriched under specific pvalueCutoff...
⚠️ No significant MSigDB GSEA pathways found.
NULL
In [55]:
library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)
# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C5")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)
# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID # for ORA
# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({
msig_enrich <- enricher(
gene = gene_list,
TERM2GENE = C2_t2g
)
if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {
write.table(msig_enrich@result,
file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
dev.off()
return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
} else {
cat("⚠️ No significant MSigDB pathways found in ORA.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
return(NULL)
})
# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({
msig_gsea <- GSEA(
geneList = gene_list2,
TERM2GENE = C2_t2g,
pvalueCutoff = 0.05
)
if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {
write.table(msig_gsea@result,
file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
sep = "\t", row.names = FALSE, quote = FALSE)
png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
dev.off()
return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
} else {
cat("⚠️ No significant MSigDB GSEA pathways found.\n")
return(NULL)
}
}, error = function(e) {
cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
return(NULL)
})
# === Display plots ===
options(repr.plot.width = 10, repr.plot.height = 10)
if (!is.null(result) && !is.null(result2)) {
options(repr.plot.width = 12, repr.plot.height = 6)
plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result)
} else if (!is.null(result2)) {
options(repr.plot.width = 7, repr.plot.height = 6)
print(result2)
} else {
cat("⚠️ No MSigDB enrichment plots to display.\n")
}
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019). preparing geneSet collections... GSEA analysis... no term enriched under specific pvalueCutoff...
⚠️ No significant MSigDB GSEA pathways found.
NULL
In [ ]: